This file documents how to replicate the tables and figures in Rodrigue, Sheng and Tan (2020), “The Curious Case of the Missing Chinese Emissions.”  It also includes the location and contact information for the original source data.

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A. Markup Estimation
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1. "production_function.do"

This do file estimates firm-level production function. With the estimated production function, we can recover firm-level (also firm-product-level) markups and marginal costs. This program contains the following 5 parts:

Part I (line 1 to line 44): This part of the code merges several datasets needed to estimate the production function. 

Part II (line 52 to line 146): This part cleans the merged data from Part I to keep single-product firms (as DeLoecker et al., 2016). We use the subsample of single-product firms to recover the industry-specific elasticities. 

Part III (line 156 to line 288): Production function estimation using the subsample of single-product firms. 

Part IV (line 305 to line 583): Recovers the input allocation share, rho, as in DeLoecker et al. (2016) for firms that produce multiple products. In this part, we need to use 
(1). MATLAB, the programs contain: "control11.m", "runrho11.m" and "rho_solve11.m";
(2). "processrhos.do": collects the estimated input allocation share parameters from the MATLAB routines.

Part V (line 602 to the end): Recovers firm-level markups and marginal costs for both single and multi-product firms. 

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2. "control11.m", "runrho11.m" and "rho_solve11.m"

These .m files compute the input allocation share parameters, rho, in MATLAB.

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3. "processrhos.do"

Collects the input allocation share, rho, for every multi-product firm. 

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Finally, the production function estimation in "production_function.do" will generate a dataset entitled "Single_technology.dta" for the subsequent decomposition exercises.

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B. Empirical Results
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4. "Tables(1-3).do"
Replicates the results in Tables 1-3.

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5. "Table4_conventional_decomposition"
Replicates the results in Table 4:
(1). line24-line 110 generate the data used for this decomposition;
(2). line 113 - 369 generates the decomposition results. 

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6. "Tables (5-7)"
Replicates the results in Tables 5-7.
(1) lines 1-94: generates the data for this decomposition;
(2) lines 700-758: generates the decomposition results for "Single-technology" in Tables 5-7;

(3) lines 775-end : generates the decomposition results for "multi-technology" in Tables 5-7;


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B2: Empirical Results for Appendix
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A1: app_tables(D1-D2): replicate tables D1-D2 in the appendix;

(1) lines 1-176: generate the summary statistics for use;
(2). lines 188-213: replicates table D1
(3). lines 230-end: replicaes table D2
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A2: app_tables(H1-H4): replicate tables H1-H4 in the Appendix;

(1). lines 38- 216: replicates  table H1;
(2). lines 236-396: replicates table H2;
(3). lines 467-636: replicates table H3;
(4). lines 652-end: replicates table H4.

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A3: app_tables(H5-H7): replcates tables H5-H7 in the Appendix;

(1) lines 1- lines 648: replicates the terms in Tables H5-H7 for Single-technology;
(2) lines 692-end: replicates the terms in Tables H5-H7 for Multiple-technology;

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A4: app_table_J1: replicate Table J1 in the appendix


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C. Data files
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1. "Multiple-technology.dta"
The production function is estimated while allowing for multiple-techonologies in each industry.

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2. "Single-technology.dta"
The production function is estimated assuming a single-techonology in each industry.

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3. "single_decomposition.dta"
This dataset used to replicate the decomposition in Tables 7-8 and 14-15 under the single-technology assumption in each industry.

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4. "multiple_decomposition.dta"
This dataset used to replicate the decomposition in Tables 7-8 and 14-15 under the multiple-technology assumption in each industry.

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5. "ownership_2000-2005.dta"
This dataset is used to capture differences across firm-level ownership, which is used in appendix Tables 10-13.

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6. "summary.dta"
This dataset is used to report the summary statistics for key variables across our primary data sources and the matched sample. It will generate Tables D1-D2 in the Appendix.

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7. "3_term_decomposition.dta"

This dataset is used to generate the total contribution of scale, composition and technique effect based on (1). revenue; and (2) cost. It will generate Table 4 in the main text.



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D: Source Data Contact Information
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1. Manufacturing and Production Data

The manufacturing and production surveys are collected by the National Bureau of Statistics and is housed at a numerous Chinese universities.  A license to employ the data for research purposes was formally purchased by Nankai University and the raw data is housed onsite. To use the dataset through Nankai University, the reader can contact:

The Economic Experiment Teaching Center at Nankai University, 300071;
tel: +86-022-23509074; or +86-022-23508986;
Web: https://econlab.nankai.edu.cn/
email: mwtyq@nankai.edu.cn (Yuqing Tu) 

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2. Environmental Data

Again, a license to employ the data for research purposes was formally purchased by Nankai University and the raw data is housed onsite. The same university contact information as listed above in (1) applies for this data set. 

The raw data was purchased by Nankai University from Beijing Century Jialan Technology and Trade Development Co., Ltd.  web: http://www.11467.com/beijing/co/483263.htm


